




'''simple docstring'''


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


UpperCAmelCase_					=   re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase_					=   10
UpperCAmelCase_					=   2_56


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  if len(a_      ) < MIN_NUM_TOKENS:
    return None
  __a								=       MinHash(num_perm=a_      )
  for token in set(a_      ):
    min_hash.update(token.encode()      )
  return min_hash


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  return {t for t in NON_ALPHA.split(a_      ) if len(t.strip()      ) > 0}





class  __lowercase    :







   def __init__(						self   ,				*,
       UpperCamelCase = 0.85   ,				)					->     List[str]:
     __a								=       duplication_jaccard_threshold
     __a								=       NUM_PERM
     __a								=       MinHashLSH(threshold=self._duplication_jaccard_threshold   ,				num_perm=self._num_perm   )

     __a								=       defaultdict(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       self._index.query(UpperCamelCase   )
     if code_key in self._index.keys:
       print(f"Duplicate key {code_key}"   )
       return

     self._index.insert(UpperCamelCase   ,				UpperCamelCase   )
     if len(UpperCamelCase   ) > 0:
       for base_duplicate in close_duplicates:
         if base_duplicate in self._duplicate_clusters:
           self._duplicate_clusters[base_duplicate].add(UpperCamelCase   )
           break
       else:
         self._duplicate_clusters[close_duplicates[0]].add(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[List[Dict]]:
     __a								=       []
     for base, duplicates in self._duplicate_clusters.items():
       __a								=       [base] + list(UpperCamelCase   )
       # reformat the cluster to be a list of dict
       __a								=       [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
       duplicate_clusters.append(UpperCamelCase   )
     return duplicate_clusters







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       self.get_duplicate_clusters()
     with open(UpperCamelCase   ,				'w'   ) as f:
       json.dump(UpperCamelCase   ,				UpperCamelCase   )


def     SCREAMING_SNAKE_CASE							(   a_						:				int      ):
  __a						,							__a								=       element
  __a								=       get_min_hash([t for t in NON_ALPHA.split(data['content']      ) if len(t.strip()      ) > 0]      )
  if min_hash is not None:
    return (index, data["repo_name"], data["path"]), min_hash


def     SCREAMING_SNAKE_CASE							(   a_						:				Type[Dataset]      ):
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash					,       ThreadedIterator(a_					,       max_queue_size=1_0000      )					,       chunksize=100					,       ):
      if data is not None:
        yield data


def     SCREAMING_SNAKE_CASE							(   a_						:				Type[Dataset]					,       a_						:				float      ):
  __a								=       DuplicationIndex(duplication_jaccard_threshold=a_      )

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a_      )      )					,       max_queue_size=100      )      ):
    di.add(a_					,       a_      )

  # Returns a List[Cluster] where Cluster is List[str] with the filenames.
  return di.get_duplicate_clusters()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				str      ):
  __a								=       get_tokens(a_      )
  __a								=       get_tokens(a_      )
  return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


UpperCAmelCase_					=   None


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				List[str]      ):
  __a								=       []
  for elementa in cluster:
    __a								=       _shared_dataset[elementa['base_index']]['content']
    for elementa in extremes:
      __a								=       _shared_dataset[elementa['base_index']]['content']
      if jaccard_similarity(a_					,       a_      ) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      __a								=       1
      extremes.append(a_      )
  return extremes


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				List[Any]					,       a_						:				Optional[int]      ):
  global _shared_dataset
  __a								=       dataset
  __a								=       []
  __a								=       partial(_find_cluster_extremes_shared					,       jaccard_threshold=a_      )
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            a_					,       a_					,       )					,       total=len(a_      )					,       ):
      extremes_list.append(a_      )
  return extremes_list


def     SCREAMING_SNAKE_CASE							(   a_						:				Type[Dataset]					,       a_						:				float = 0.85      ):
  __a								=       make_duplicate_clusters(a_					,       a_      )
  __a								=       {x['base_index'] for cluster in duplicate_clusters for x in cluster}
  __a								=       {}
  __a								=       find_extremes(a_					,       a_					,       a_      )
  for extremes in extremes_clusters:
    for element in extremes:
      __a								=       element
  __a								=       duplicate_indices - set(extreme_dict.keys()      )
  __a								=       dataset.filter(lambda a_					,       a_      : idx not in remove_indices					,       with_indices=a_      )

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      __a								=       element['base_index'] in extreme_dict
      if element["is_extreme"]:
        __a								=       extreme_dict[element['base_index']]['copies']

  print(f"Original dataset size: {len(a_      )}"      )
  print(f"Number of duplicate clusters: {len(a_      )}"      )
  print(f"Files in duplicate cluster: {len(a_      )}"      )
  print(f"Unique files in duplicate cluster: {len(a_      )}"      )
  print(f"Filtered dataset size: {len(a_      )}"      )

  return ds_filter, duplicate_clusters









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



